How Racial Biases Affect Lending
If recent history has taught us anything, it’s that racial biases have seeped into and infected every part of our lives. While progress has been made in some ways over the last few decades, recent statistics regarding opportunities for minorities in America are harrowing. Not only do minorities in America — and African Americans in particular — face serious hurdles in employment and education when compared to white Americans, but they face plenty of other hurdles, too.
For example, Black students represented only 15% of total U.S. student enrollment during the 2015-2016 school year, but they made up 35% of students suspended once, 44% of students suspended more than once, and 36% of students expelled. The US Department of Education concluded that this disparity is “not explained by more frequent or more serious misbehavior by students of color,” meaning that the most likely culprit is deeply engrained racial biases at play.
Black Americans also face higher rates of arrest, conviction, and police interventions, as well as longer and harsher sentences — and violence or death at the hands of law enforcement. For example, 88% of police stops that occurred in New York City in 2018 involved people of color, but only 10% involved white people.
Racial biases hardly just affect education, employment, and the justice system, though. Biases against minorities also affect banking and lending practices, too. Statistics show that minority borrowers — African Americans and Latinos in particular — often face higher-priced auto loans and mortgage interest rates, and can even get denied for credit or loan products, despite being qualified applicants.
Let’s take a look at how and why racial biases are affecting lending — and, most importantly, what we can do to fix this broken system.
Racial biases in lending: the statistics
When it comes to lending statistics for African American and Latino applicants, the disparities are clear. Recent research has shown that a large percentage of minority applicants pay more for loans than their white counterparts, even when their financial pictures are comparable.
Take, for example, this 2018 study by the researchers at University of California, Berkeley, which found that both online and face-to-face mortgage lenders charge higher interest rates to African American and Latino borrowers. That amounts to minority homebuyers paying up to half a billion dollars more in interest every year over white borrowers with comparable credit scores, according to the UC Berkeley researchers.
The researchers at Berkeley also found:
- Black and Latino borrowers pay 5.6 to 8.6 basis points higher interest on purchase loans than white and Asian borrowers do, and 3 basis points more on refinance loans
- For borrowers, these disparities cost them $250M to $500M annually
- For lenders, this amounts to 11% to 17% higher profits on purchase loans to minorities, based on the industry average 50-basis-point profit on loan issuance
This is an issue for obvious reasons — comparable applicants are paying more for loans simply based on the color of their skin, and the price is extremely high, not only when you consider the out of pocket costs — but the social costs as well.
Berkeley is hardly the first to point the issue out, though. Study after study has pinpointed issues with lending discrimination, with some dating as far back as the 1970s.
This HUD study from 1999 — which was published over two decades ago — pointed out that “minorities are less likely than whites to obtain mortgage financing and, if successful in obtaining a mortgage, tend to receive less generous loan amounts and terms,” and stated that “racial disparities in loan denial rates cannot be “explained away” by differences in creditworthiness or by technical factors affecting the analyses.”
And, the Fair Lending 2.0: A Borrower-Based Solution to Discrimination in Mortgage Lending, which was published back in 2011 by the University of Michigan Journal of Law Reform, noted that “Black borrowers consistently receive inferior mortgage products when compared to similarly situated white borrowers.” These examples are just two of many, many studies conducted on the subject over the last several decades.
Given the copious amounts of current and historical data, it’s clear that discrimination in lending has long been — and continues to be — an issue, despite the fact that laws were put in place decades ago to make it illegal for lenders to discriminate against members of historically disadvantaged groups.
The lending discrepancies don’t just occur with mortgage loans, though. A 2019 investigation by the National Fair Housing Alliance, a Washington D.C.-based nonprofit, found that minority applicants — who were more financially qualified than their white counterparts — were offered higher-priced car loans 60% of the time, costing them an extra $2,662 each over the course of the loan.
Computer-based lending: Helpful or hurtful?
While lending discrimination has historically been caused by human prejudice, the introduction of algorithm-based lending practices hasn’t done much to stem the issue. Theoretically, computer-based lending decisions should help to eliminate the human biases that surface in face-to-face lending, but the 2018 study from Berkeley, referenced above, takes into account the lending decisions made by computer-based algorithms — and the data still indicates that minorities are facing prejudice.
In other words, it turns out that it’s not only human lending decisions that are biased, but computer-based lending programs are, too.
“The mode of lending discrimination has shifted from human bias to algorithmic bias,” study co-author Adair Morse, a finance professor at UC Berkeley’s Haas School of Business, said following the study. “Even if the people writing the algorithms intend to create a fair system, their programming is having a disparate impact on minority borrowers — in other words, discriminating under the law.”
These machine-based biases are occurring because the algorithms — whether purposely or inadvertently — are built to assume that minority borrowers won’t shop around as much for other lenders or interest rates when they’re taking out a loan.
Algorithms identify minority buyers in a number of different ways — by identifying applicants in minority-dominated zip codes or by targeting areas with fewer financial services than average, or by some other differentiating factor — and then charge them more for their loan, despite the applicants having comparable credit scores and financial pictures to white applicants.
Even more troubling, though, is that not only are computer lending algorithms identifying and penalizing African American and Latino borrowers — it’s occurring at a time when fintech, and computer-based lending, rules the roost. We already know that human-based lending produces biases, but so do the algorithms that are meant to help eliminate said biases. And, in turn, a large portion of African American or Latino homebuyers will likely face some discrimination during the lending process — human lender or otherwise.
The fact that computer-based lending isn’t reducing lender discrimination raises a ton of questions, including legal questions about statistical discrimination in the fintech era. It also points to the potential for widespread violations of U.S. fair lending laws.
While the issues with algorithm-based discrimination are harrowing, there are some bright spots in the fintech research that has been compiled over the last few years. While African American and Latino borrowers are more likely to be charged higher rates for borrowing money, the Berkeley researchers found that fintech lenders — i.e. algorithms — didn’t discriminate when it comes to accepting minority applicants. Traditional face-to-face lenders, on the other hand, did: unlike algorithm-based lending, face-to-face lenders were still 5% more likely to reject African American and Latino borrowers.
So, what now?
It’s pretty clear from the statistics and data that there’s a long road ahead for leveling the lending playing field for minorities. Despite the introduction of fintech innovations like algorithms and computer-based lending decisions, African American homeownership numbers have not increased in the last 35 years, and minorities continue to face an uphill battle when it comes to securing loans. The enforcement of anti-discrimination rules in housing and mortgage markets is a crucial component of changing the current lending landscape, as are the efforts by, and support for, community groups, community members and nonprofits that aim to change things for minorities in America.
Note: This piece is part 1 of a two-part series on racial biases in lending. Want to keep reading about how we can change the racial biases in lending? Click here to access it.